ML Ops Engineer

Job Title: ML Ops Engineer — Model CI/CD & Monitoring (1 Position)

About the Job

We are seeking an experienced ML Ops Engineer to own and scale our model CI/CD, deployment, monitoring and observability practices. In this role you will build reliable, reproducible ML delivery pipelines, productionize models, implement robust monitoring (data & model), and create automated workflows that ensure model quality, reliability and compliance across cloud and edge environments. The ideal candidate combines strong software/infra engineering skills with hands-on ML and monitoring experience and a pragmatic focus on production reliability.

Responsibilities

• Design & implement end-to-end model CI/CD pipelines: versioning, testing, validation, packaging, and automated deployment.

• Build reproducible training & inference workflows (Airflow, GitHub Actions/GitLab CI, Argo, Tekton, or similar).

• Integrate model registries and lineage (MLflow, Feast, Tecton, or equivalent) and manage model lifecycle (staging → canary → production → rollback).

• Containerize models and serve them reliably (KServe, BentoML, Seldon, TorchServe, or custom solutions) on Kubernetes and/or edge devices.

• Implement model & data monitoring: drift detection, performance regression, calibration, latency, throughput, and business-metric SLOs.

• Create observability, metrics and alerting (Prometheus, Grafana, OpenTelemetry) and integrate with incident workflows.

• Automate data validation and feature checks (Great Expectations, TFDV) and ensure data quality gates in CI.

• Collaborate with data engineers, ML researchers, firmware/hardware teams and product owners to integrate models into production systems.

• Harden deployments for security, privacy and compliance (secrets management, RBAC, auditing).

• Drive MLOps best practices: reproducibility, experiment tracking, cost-efficient training, resource management and documentation.

• Mentor team members and contribute to architecture decisions and tooling choices.

Qualifications

• BSc/MSc in Computer Science, Engineering, or related field — or equivalent practical experience.

• 3+ years experience in MLOps / Production ML / DevOps roles; demonstrable track record delivering ML models to production.

• Strong programming skills in Python; familiarity with ML frameworks (PyTorch, TensorFlow, scikit-learn).

• Hands-on experience with containerization and orchestration: Docker, Kubernetes, Helm.

• Experience with CI/CD systems (GitHub Actions, GitLab CI, Jenkins, ArgoCD) and IaC tools (Terraform, CloudFormation).

• Familiarity with model registries, experiment tracking and feature stores (MLflow, DVC, Feast, etc.).

• Practical knowledge of monitoring / observability stacks (Prometheus, Grafana, OpenTelemetry) and alerting/incident response.

• Experience implementing model/data drift detection, calibration and A/B/canary deployments.

• Comfortable with cloud platforms (AWS/GCP/Azure) and serverless/edge deployment patterns.

• Strong problem-solving, documentation and cross-team communication skills.

• Bonus: experience with on-device inference, real-time inference, regulatory/compliance requirements, or domain experience (energy, IoT, industrial telemetry).

What We Offer

• Leadership role shaping ML delivery and production reliability at scale.

• Cross-functional team of engineers, data scientists and product owners.

• Competitive compensation and support for training, conferences and certifications.

• Flexible/remote working options and opportunity to work on real-world ML systems with measurable impact.

• Access to cloud credits, tooling budgets and pilot deployment resources.

How to Apply

Please submit the following to with subject line "ML Ops Engineer — Model CI/CD & Monitoring":

1. CV (max 2 pages)

2. Cover letter (1 page) describing relevant MLOps experience and why you're excited about this role

3. Links to portfolio, GitHub, or examples of production ML/MLOps projects (PDF or URLs)

4. Two references (name, role, contact)

Shortlisted candidates may be asked to complete a brief technical exercise or present a past deployment/architecture. We welcome applicants from diverse backgrounds and those who combine strong engineering discipline with practical ML production experience.